We present a corrosion forecasting engine that is designed to predict internal pipeline corrosion under both current and future operating scenarios. At its core, a pipeline digital twin is made available that integrates real-time and historical data, various inspection reports and maintains a continuously updated digital replica of the physical pipeline system. This twin provides leverages an in-house internal corrosion model, calibrated using field data such as in-line inspection reports or corrosion coupon measurements. This dynamic calibration aligns the mathematical model with actual field performance, thus enhancing accuracy and trustworthiness.

A key innovation is the engine’s interactive scenario-building capability. Users can define hypothetical or planned changes in the pipeline conditions – such as variations in flow rates, pressure, temperature or composition changes such as water cut, chemical dosing – and visualize the resulting impact on corrosion rates and wall thickness degradation over time horizon of choice. This empowers the integrity management team with informed decision-making for maintenance scheduling and operational strategy.

The forecasting engine operates entirely within a cloud environment, unlocking data-analytics and seamless integration with hydraulic modeling. By coupling flow modeling and field data with corrosion mechanisms, the digital twin generates high-resolution wall thickness forecasts, helping integrity management teams identify high-risk zones proactively. The digital-first, data-calibrated approach empowers engineers with predictive insights, and enables cost-effective, risk-informed asset management.